Needs are changing with time and so the technology. Self-driving cars, or Siri, what do these have in common? Well! they are... moreNeeds are changing with time and so the technology. Self-driving cars, or Siri, what do these have in common? Well! they are largely the examples of machine learning being utilized as a part of this real world. Machine learning today has changed the way we look and the way we interact with the technology.
Even the healthcare sector is getting transformed by the ability to record massive amounts of information about individual patients, the enormous volume of data being collected is impossible for human to analyze. Machine learning provides a way to automatically find patterns and reason about data, which enables healthcare professionals to move to personalized care.
There are many possibilities for how machine learning can be used in healthcare, and for this reason we have outlined the course of Applied Machine Learning in healthcare only for you that offers a hands-on experience on how to build actual projects using the Machine Learning Datasets.
This course covers five python implementations with the project series, that will explore medically related data sets by solving the critical issues using state of the art machine learning techniques.
This course will give you the hands on experience working on the Breast cancer detection project. We will be training a K nearest algorithm model as a support vector machine to predict whether the cell is cancerous or not.
The second project is followed with the Diabetes onset prediction. We will be focusing on Neural networks that will help users to learn similar implementation on wide variety of problems.
The third project will be the DNA classification project, here we will using the sequence of E-Coli(Escherichia coli)as our input data, by creating a classification based machine learning algorithm.
The fourth project will be the Heart disease prediction project. In this course we will be building a training algorithm that predicts coronary heart disease.
The fifth project will take you to the Autism screening, that will cover several supervised learning techniques to diagnose Autistic Spectrum Disorder based on behavioral features.
The bonus part of this projects will be Data Pre Processing. This course will cover handling the type of data i.e. typical to the health care field in addition to some cool machine learning techniques such as Neural Networks and other Supervised learning techniques such as simple linear classifiers.
Get started in machine learning with this epic intro course that makes machine learning simpler and easy to understand! Don’t let time slip from your hand. Enroll your name now and learn a bit on how Machine Learning can be used in the health care field.
Who this course is for:
Anyone who wants to learn Machine learning and its application in the healthcare and life sciences will find this course very useful less
This course will teach you how to work with health data, using machine learning models to find actionable insights.
Through... moreThis course will teach you how to work with health data, using machine learning models to find actionable insights.
Through a step-by-step guided case study, you will learn practical skills that you can apply immediately!
We will use a case study: Opioid Abuse Prediction for a clinic
Topics we will cover:
Health Data (sources, types, features, error handling)
Logistics of machine learning
What predictive model features are, and how to create them
A statistical primer, highlighting key machine learning models and concepts
Build a decision tree, logistic regression and random forest through
Opioid abuse prediction case study
KNIME (a free machine learning software, no coding required!)
Assess model performance
Output presentation and implementation
Who this course is for:
Beginner to Intermediate analysts of health data
Public health/Epidemiology/Bioinformatics analysts
Actuarial/Statistical analysts less
The most commonly available and widely used type of data in healthcare is claims data. Claims data is sometimes also called... moreThe most commonly available and widely used type of data in healthcare is claims data. Claims data is sometimes also called ‘billing data’ or administrative data. The reason why claims data is the most large scale, reliable and complete type of big data in healthcare is rather straightforward. It has to do with reimbursement, that is, the payment of health care goods and services depends on claims data. Healthcare providers may not always find the time to fill in all required paperwork in healthcare, but they will always do that part of their administration on which their income depends. Thus, in many cases, analyzing healthcare claims data is a much more pragmatic alternative for extracting valuable insights.
Claims data allows for the analysis of many non-biological elements pertaining to the organization of health care, such as patient referral patterns, patient registration, waiting times, therapy adherence, health care financing, patient pathways, fraud detection and budget monitoring. Claims data also allows for some inferences about biological facts, but these are limited when compared to medical records.
By following this course, students will gain a solid theoretical understanding of the purpose of healthcare claims data. Moreover, a significant portion of this course is dedicated to the application of data science and health information technology (Healthcare IT) to obtain meaningful insights from raw healthcare claims data.
This course is for professionals that (want to) work in health care organizations (providers and payers) that need to generate actionable insights out of the large volume of claims data generated by these organizations. In other words, people that need to apply data science and data mining techniques to healthcare claims data.
Examples of such people are: financial controllers and planners, quality of care managers, medical coding specialists, medical billing specialists, healthcare or public health researchers, certified electronic health records specialist, health information technology or health informatics personnel, medical personnel tasked with policy, personnel at procurement departments and fraud investigators. Finally, this course will also be very useful for data scientists and consultants that lack domain knowledge about the organization of healthcare, but somehow got pulled into a healthcare claims data project.
The instructor of this course is Dennis Arrindell, MSc., MBA. Dennis has a bachelor’s degree in Public Health, a master’s degree in Health Economics and a Master’s degree in Business Administration.
Upon completion of this course, students will be able to contribute significantly towards making healthcare organizations (providers and payers) more data driven.
What this course is NOT about:
- Although we will be applying some important statistics and machine learning concepts, this course is NOT about statistics or machine learning as a topic on itself.
- Although we will be using multiple software tools and programming languages for the practical parts of this course, this course is NOT about any of these tools (Excel, SQL, Python, Celonis for process mining) as topics on themselves.
Who this course is for:
This course is for professionals that are involved with healthcare providers and health insurers that need to generate actionable insights out of the large volume of claims data generated by these organizations. Examples are: financial controllers and planners, quality of care managers, medical coding specialists, medical billing specialists, healthcare or public health researchers, certified electronic health records specialist, health information technology or health informatics personnel, medical personnel tasked with policy, personnel at procurement departments and fraud investigators. Finally, this course will also be very useful for data scientists and consultants that lack domain knowledge about the organization of healthcare, but somehow got pulled into a healthcare claims data project less
The Business Analytics Capstone Project gives you the opportunity to apply what you've learned about how to make data-driven... moreThe Business Analytics Capstone Project gives you the opportunity to apply what you've learned about how to make data-driven decisions to a real business challenge faced by global technology companies like Yahoo, Google, and Facebook. At the end of this Capstone, you'll be able to ask the right questions of the data, and know how to use data effectively to address business challenges of your own. You’ll understand how cutting-edge businesses use data to optimize marketing, maximize revenue, make operations efficient, and make hiring and management decisions so that you can apply these strategies to your own company or business. Designed with Yahoo to give you invaluable experience in evaluating and creating data-driven decisions, the Business Analytics Capstone Project provides the chance for you to devise a plan of action for optimizing data itself to provide key insights and analysis, and to describe the interaction between key financial and non-financial indicators. Once you complete your analysis, you'll be better prepared to make better data-driven business decisions of your own less
Accounting Analytics explores how financial statement data and non-financial metrics can be linked to financial performance.... moreAccounting Analytics explores how financial statement data and non-financial metrics can be linked to financial performance. In this course, taught by Wharton’s acclaimed accounting professors, you’ll learn how data is used to assess what drives financial performance and to forecast future financial scenarios. While many accounting and financial organizations deliver data, accounting analytics deploys that data to deliver insight, and this course will explore the many areas in which accounting data provides insight into other business areas including consumer behavior predictions, corporate strategy, risk management, optimization, and more. By the end of this course, you’ll understand how financial data and non-financial data interact to forecast events, optimize operations, and determine strategy. This course has been designed to help you make better business decisions about the emerging roles of accounting analytics, so that you can apply what you’ve learned to make your own business decisions and create strategy using financial data. less
Accounting Analytics explores how financial statement data and non-financial metrics can be linked to financial performance.... moreAccounting Analytics explores how financial statement data and non-financial metrics can be linked to financial performance. In this course, taught by Wharton’s acclaimed accounting professors, you’ll learn how data is used to assess what drives financial performance and to forecast future financial scenarios. While many accounting and financial organizations deliver data, accounting analytics deploys that data to deliver insight, and this course will explore the many areas in which accounting data provides insight into other business areas including consumer behavior predictions, corporate strategy, risk management, optimization, and more. By the end of this course, you’ll understand how financial data and non-financial data interact to forecast events, optimize operations, and determine strategy. This course has been designed to help you make better business decisions about the emerging roles of accounting analytics, so that you can apply what you’ve learned to make your own business decisions and create strategy using financial data less
This course is designed to impact the way you think about transforming data into better decisions. Recent extraordinary... moreThis course is designed to impact the way you think about transforming data into better decisions. Recent extraordinary improvements in data-collecting technologies have changed the way firms make informed and effective business decisions. The course on operations analytics, taught by three of Wharton’s leading experts, focuses on how the data can be used to profitably match supply with demand in various business settings. In this course, you will learn how to model future demand uncertainties, how to predict the outcomes of competing policy choices and how to choose the best course of action in the face of risk. The course will introduce frameworks and ideas that provide insights into a spectrum of real-world business challenges, will teach you methods and software available for tackling these challenges quantitatively as well as the issues involved in gathering the relevant data less
Data about our browsing and buying patterns are everywhere. From credit card transactions and online shopping carts, to... moreData about our browsing and buying patterns are everywhere. From credit card transactions and online shopping carts, to customer loyalty programs and user-generated ratings/reviews, there is a staggering amount of data that can be used to describe our past buying behaviors, predict future ones, and prescribe new ways to influence future purchasing decisions. In this course, four of Wharton’s top marketing professors will provide an overview of key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices including Amazon, Google, and Starbucks to name a few. This course provides an overview of the field of analytics so that you can make informed business decisions. It is an introduction to the theory of customer analytics, and is not intended to prepare learners to perform customer analytics.
Course Learning Outcomes: After completing the course learners will be able to... Describe the major methods of customer data collection used by companies and understand how this data can inform business decisions Describe the main tools used to predict customer behavior and identify the appropriate uses for each tool Communicate key ideas about customer analytics and how the field informs business decisions Communicate the history of customer analytics and latest best practices at top firms less
achine learning (ML) is a branch of artificial intelligence (AI) that enables computers to self-learn and improve over time... moreachine learning (ML) is a branch of artificial intelligence (AI) that enables computers to self-learn and improve over time without being explicitly programmed. In short, machine learning algorithms are able to detect and learn from patterns in data and make their own predictions.
In traditional programming, someone writes a series of instructions so that a computer can transform input data into a desired output. Instructions are mostly based on an IF-THEN structure: when certain conditions are met, the program executes a specific action.
Machine learning, on the other hand, is an automated process that enables machines to solve problems and take actions based on past observations.
Basically, the machine learning process includes these stages:
Feed a machine learning algorithm examples of input data and a series of expected tags for that input.
The input data is transformed into text vectors, an array of numbers that represent different data features.
Algorithms learn to associate feature vectors with tags based on manually tagged samples, and automatically makes predictions when processing unseen data.
While artificial intelligence and machine learning are often used interchangeably, they are two different concepts. AI is the broader concept – machines making decisions, learning new skills, and solving problems in a similar way to humans – whereas machine learning is a subset of AI that enables intelligent systems to autonomously learn new things from data.
Project-1 Blood Donation Analysis
Project-2 Mortality Prediction In ICU Using ANN
Project-3 Kyphosis Disease Classification
Project-4 Suicide Rate Trend Analysis
Project-5 DNA Classification of Humans And Chimpanzee
Project-6 NYSE Stock Price Prediction
Project-7 RBI Resources Data Analysis
Project-8 E-signing of a loan based on financial history
This course would be immensely useful for both healthcare professionals as well as software professionals.
Healthcare... moreThis course would be immensely useful for both healthcare professionals as well as software professionals.
Healthcare professionals will be able to understand real life use-cases where they can use AI and ask their vendors to build AI enabled modules for them.
Software professionals, on the other hand, will understand the needs by seeing real life examples and will be in a better position to collect data and build AI modules for their customers.
In this course, I have covered the basic benefits of using AI, Sample analysis using a real life example, the stages in clinical care life cycle where AI can be used for intervention, What will be the data requirements and how to gather that data, AI modelling and extending those models to healthcare, how to bring the stake holders of healthcare together using AI and finally, the challenges for implementing AI in healthcare and how to resolve them. less
Evaluation of AI solutions in Healthcare.
To evaluate an AI solution beyond its predictive value, we need a framework that... moreEvaluation of AI solutions in Healthcare.
To evaluate an AI solution beyond its predictive value, we need a framework that provides criteria to evaluate the utility, feasibility, and clinical impact of an AI solution.
Machine learning is a family of statistical and mathematical modelling techniques that uses a variety of approaches to automatically learn and improve the prediction of a target objective without explicit programming. These are the systems that improve their performance in a given task through exposure to experience or data.
Different neurotransmitters have different effects on the postsynaptic cell. There's a great number of categories of neurotransmitters. Some fall in the category of amino acids, like glutamate, aspartate, glycine and D-serine. Some of them are peptides, like somatostatin, vasopressin, oxytocin, and opioid peptides. And there are other categories, that includes serotonin or epinephrine, histamine, melatonin and others. In fact, overall there are over 50 neurotransmitters that have been identified, each having slightly different neurochemistry and each having slightly different functions less
AI is an enabler in transforming healthcare delivery in terms of treatment modalities and their outcomes, electronic health... moreAI is an enabler in transforming healthcare delivery in terms of treatment modalities and their outcomes, electronic health records-based prediction, diagnosis and prognosis and precision medicine. This course will introduce you to the cutting edge advances in AI concerning healthcare by exploiting deep learning architectures.
The course aims to provide students from diverse backgrounds with both conceptual understanding and technical grounding of leading research on AI in healthcare. Highlighted topics to be covered in this course are listed below;
1. AI functionality and what is refueling AI in healthcare.
2. Deep Learning Convolutional Networks for AI Healthcare.
3. Role and Management of Big Data Computing in AI Healthcare.
4. How Hadoop and Python is cultivating AI based wellbeing.
5. AI based solutions for Neurological Diseases using Deep Learning.
6. AI for Brain Computer Interfacing and Neuromodulation.
7. AI algorithms for diagnosis, prognosis and treatment plans for Tumors.
8. How to model an AI problem in Healthcare.
9. How to create, preprocess and augment a data set for AI based Healthcare.
10. How to use transfer learning in multiclass classification healthcare problems.
11. Optimizers to be used in Deep learning Healthcare Problems.
12. Leading Convolutional Neural Networks (ALEXNET & INCEPTION) and validation indices.
12. Recurrent Neural Networks extending to Long Short Term Memory.
13. An understanding of Green AI.
14. Implementations of Neural Networks in Keras and Pytorch.
15. Introduction to Quantum Machine Learning.
16. Algorithms related to Quantum Machine Learning in TensorFlow Quantum and Qiskit.
17. Artificial Intelligence in Robotics.
18. Artificial Intelligence in Smart Chatbots.
19. Impact of AI in business analytics.
20. AI in media and creative industries.
21. AI based advertisements for maximum clicks.
22. AI for the detection of Misinformation Detection.
23. Extraction of Fashion Trends using AI.
24. AI for emotion detections during Covid- 19 less
In this course, you’ll learn about the emerging technologies in Artificial Intelligence and Machine Learning that are... moreIn this course, you’ll learn about the emerging technologies in Artificial Intelligence and Machine Learning that are utilized in InsurTech and Real Estate Tech. Professor Chris Geczy of the Wharton School has designed this course to help you navigate the complex world of insurance and real estate tech, and understand how FinTech plays a role in the future of the industry. Through study and analysis of Artificial Intelligence and Machine Learning, you’ll learn how InsurTech is redefining the insurance industry. You’ll also explore classifications of insurtech companies and the size of the InsurTech, Real Estate Tech, and AI markets. You will also explore FinTech specialties with Warren Pennington from Vanguard. By the end of this course, you’ll be able to identify emerging technologies of AI, Machine Learning, and Financial Technologies from a variety of insurtech and real estate tech companies and its impact in the future of finance and investments.